Driving Style Classification and the Effectiveness of Advanced Driving Assistance Systems: Differences between Teen and Adult Drivers

Abstract

Advanced driving assistance systems (ADAS) are designed to reduce potential crash risks and enhance driving safety. However, drivers’ interactions with ADAS may vary depending on their individual driving styles and characteristics. This study proposes a novel approach to classifying driving styles and explores how age and gender affect interactions with ADAS. The study utilized two naturalistic driving data sets comprising 148 drivers from four age groups: teens; younger adults; middle-aged adults; and older adults. Data were collected during two periods: baseline (without ADAS); and treatment (with ADAS). First, the K-means clustering algorithm was employed to divide trips into one conservative and two aggressive groups based on three driving behavior metrics: tailgating; speeding; and lane-changing. The aggressive-trip ratios were then calculated for each driver during each of the two periods. The Bayesian Gaussian mixture model was applied to determine the threshold values of the aggressive-trip ratios to classify drivers as conservative, moderate, or aggressive during each period. This allowed for identifying changes in driving style upon the activation of ADAS. The subsequent multinomial logistic regression model results showed that driving styles vary across age groups, with teens being the most aggressive drivers. Certain changes in driving style were observed, with some conservative drivers becoming aggressive or moderate and some aggressive drivers becoming conservative or moderate, but these differences were statistically non-significant. The findings of this study indicate that warning-based ADAS may not elicit significant changes in driving style, particularly among teenage drivers who are consistently the most aggressive drivers.

Reliability-based analysis of horizontal curve design by evaluating the impact of vehicle automation on roadway departure crashes and safety performance

“…The progressive evolution of ADAS indicates the initiation of fully automated vehicles, introducing novel challenges and intricacies in roadway safety. The diverse proficiencies and experiences of drivers, coupled with varying automation levels, necessitate a comprehensive understanding and meticulous addressing of the limitations inherent to automation systems [ 14 ]. Recognizing and addressing the nuanced challenges are paramount to seamlessly integrating these groundbreaking technologies into our prevailing transportation frameworks, vehicles on the road today have varying Levels of Automation (LoA) (0–3) that require the full attention of the human operator due to their limitations [ 15 ].…”